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Endogenous Prediction of Bankruptcy using a Support Vector Machine

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  • Zazueta, Jorge
  • Heredia, Andrea Chavez
  • Zazueta-Hernández, Jorge

Abstract

We build a global bankruptcy prediction model using a support vector machine trained only on firms' endogenous information in the form of financial ratios. The model is tested not only on entirely random unseen data but on samples taken from specific global regions and industries to test for prediction bias, achieving satisfactory prediction performance in all cases. While support vector machines are not easily interpretable, we explore variable importance and find it consistent with economic intuition.

Suggested Citation

  • Zazueta, Jorge & Heredia, Andrea Chavez & Zazueta-Hernández, Jorge, 2021. "Endogenous Prediction of Bankruptcy using a Support Vector Machine," SocArXiv ehpt7, Center for Open Science.
    • Handle: RePEc:osf:socarx:ehpt7
    DOI: 10.31219/osf.io/ehpt7
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
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